基于滤波和优化的惯性/里程计组合导航精度对比分析

Comparative Analysis of SINS/ODO Integrated Navigation Accuracy between Filtering and Optimization Method

在广泛使用的惯性/里程计组合导航应用中,一贯的方法是使用Kalman滤波器进行组合导航状态估计,展现出了强大的工程适用性。然而其对于历史状态没有修正,导致整体导航精度受限。而基于优化的方法可以平滑整体组合导航的轨迹,有望提升惯性/里程计组合导航的精度和鲁棒性。将优化方法与滤波方法在惯性/里程计组合导航应用中进行对比,分析在不同条件下优化算法和滤波算法的优劣。试验表明,基于优化的算法在含有稀疏位置观测的条件下能够比滤波算法定位精度更高。但是,无位置观测的条件下,滤波算法更加稳定和精确。可以得到:基于滤波的算法更加简单稳定,更适用于工程实践;基于优化的算法模型更加复杂,在观测约束更多的条件下能够得到更好状态估计结果。

In widely used SINS/ODO integrated navigation applications,the conventional method is to use Kalman filtering for integrated navigation state estimation,demonstrating strong engineering applicability.However,its historical status cannot be corrected leading to limited overall navigation accuracy.The optimization-based method can smooth the trajectory,which is expected to improve the accuracy and robustness of SINS/ODO integrated navigation.In this paper,the optimization method and traditional filtering method are compared in SINS/ODO integrated navigation applications.The advantages and disadvantages of the two methods are analyzed under different conditions.The result shows that the optimization method can achieve higher positioning accuracy than the filtering method under the condition of sparse position observation.However,the filtering method can be more stable and accurate without position observation.The conclusion of comparative analysis is that the method based on filtering is simpler and more stable,and more suitable for engineering practice,while the method model based on optimization is more complex and can get better state estimation results under the condition of more constraints.